Original paper

SSRN-id1810985.pdf858.6KB

Abstract

Using a large sample of countries and 60 years of data, the authors found a strong and intuitive link between demographic transitions and both GDP growth and capital market returns. Unlike previous researchers, who used ad hoc and restrictive demographic variables, the authors imposed a smooth and parsimonious polynomial curve across all age groups. They also performed robustness checks and produced forecasts for the coming decade, with all the necessary caveats.

Keywords: demography, demographics

Trading rules

Target investment: Focus on 15 developed countries (Australia, Austria, Belgium, Canada, Denmark, France, Germany, Italy, Japan, Netherlands, Spain, Sweden, Switzerland, UK, US).
Evaluate the proportion of adults over 65 in every nation.
Determine the mean and variability for this proportion.
Adopt a long position in nations where the elderly (population >65) constitute a percentage that's at least 1 standard deviation under the mean.
Adopt a short position in nations where the elderly represent a percentage that's at least 1 standard deviation over the mean.
Adjust portfolio positions once every year.

Python code

Backtrader

import backtrader as bt
import pandas as pd

class DemographicStrategy(bt.Strategy):
    def __init__(self):
        self.country_data = pd.DataFrame({
            'country': ['Australia', 'Austria', 'Belgium', 'Canada', 'Denmark', 'France', 'Germany', 'Italy', 'Japan', 'Netherlands', 'Spain', 'Sweden', 'Switzerland', 'UK', 'US'],
            'percent_over_65': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]  # Fill in actual data
        })

    def next(self):
        if self.datetime.date().month != 12:
            return
        mean_over_65 = self.country_data['percent_over_65'].mean()
        std_over_65 = self.country_data['percent_over_65'].std()
        long_countries = self.country_data[self.country_data['percent_over_65'] < mean_over_65 - std_over_65]
        short_countries = self.country_data[self.country_data['percent_over_65'] > mean_over_65 + std_over_65]
        # Allocate equal weights to long and short positions        long_weight = 1.0 / len(long_countries)
        short_weight = -1.0 / len(short_countries)
        # Adjust positions
        for d in self.getdatanames():
            country = d.split('.')[0]
            if country in long_countries['country'].values:
                self.order_target_percent(data=d, target=long_weight)
            elif country in short_countries['country'].values:
                self.order_target_percent(data=d, target=short_weight)
            else:
                self.order_target_percent(data=d, target=0.0)

cerebro = bt.Cerebro()
# Add data feeds for each country's ETF
# Data should be in a format that includes the country name in the dataname field
# Example: cerebro.adddata(bt.feeds.YahooFinanceData(dataname='Australia.ETF', ...))
# ...
cerebro.addstrategy(DemographicStrategy)
cerebro.run()

Please note that you will need to replace the ‘percent_over_65’ values in the ‘country_data’ DataFrame with the actual data. Also, make sure to add the data feeds for each country’s ETF to the ‘cerebro’ object before running the strategy.

Demographic Changes, Financial Markets, and the Economy

Original paper

Abstract

Trading rules

Python code

Backtrader